




"""simple docstring"""

import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
  raise Exception("""requires gluonnlp == 0.8.3""")

if version.parse(mx.__version__) != version.parse("""1.5.0"""):
  raise Exception("""requires mxnet == 1.5.0""")

logging.set_verbosity_info()
__magic_name__				     =     logging.get_logger(__name__)

__magic_name__				     =     """The Nymphenburg Palace is a beautiful palace in Munich!"""




def     _A					( __lowercase							,     __lowercase    ):





       """simple docstring"""



       lowerCamelCase__          =						{
           'attention_cell': 'multi_head',
           'num_layers': 4,
           'units': 1024,
           'hidden_size': 768,
           'max_length': 512,
           'num_heads': 8,
           'scaled': True,
           'dropout': 0.1,
           'use_residual': True,
           'embed_size': 1024,
           'embed_dropout': 0.1,
           'word_embed': None,
           'layer_norm_eps': 1e-5,
           'token_type_vocab_size': 2,
       }

       lowerCamelCase__          =						bort_4_8_768_1024_hparams

       # Let's construct the original Bort model here
       # Taken from official BERT implementation, see:
       # https://github.com/alexa/bort/blob/master/bort/bort.py
       lowerCamelCase__          =						BERTEncoder(
           attention_cell=predefined_args["""attention_cell"""]							,     num_layers=predefined_args["""num_layers"""]							,     units=predefined_args["""units"""]							,     hidden_size=predefined_args["""hidden_size"""]							,     max_length=predefined_args["""max_length"""]							,     num_heads=predefined_args["""num_heads"""]							,     scaled=predefined_args["""scaled"""]							,     dropout=predefined_args["""dropout"""]							,     output_attention=a__							,     output_all_encodings=a__							,     use_residual=predefined_args["""use_residual"""]							,     activation=predefined_args.get("""activation"""							,     """gelu"""    )							,     layer_norm_eps=predefined_args.get("""layer_norm_eps"""							,     a__    )							,     )

       # Vocab information needs to be fetched first
       # It's the same as RoBERTa, so RobertaTokenizer can be used later
       lowerCamelCase__          =						'openwebtext_ccnews_stories_books_cased'

       # Specify download folder to Gluonnlp's vocab
       lowerCamelCase__          =						os.path.join(get_home_dir()							,     """models"""    )
       lowerCamelCase__          =						_load_vocab(a__							,     a__							,     a__							,     cls=a__    )

       lowerCamelCase__          =						nlp.model.BERTModel(
           a__							,     len(a__    )							,     units=predefined_args["""units"""]							,     embed_size=predefined_args["""embed_size"""]							,     embed_dropout=predefined_args["""embed_dropout"""]							,     word_embed=predefined_args["""word_embed"""]							,     use_pooler=a__							,     use_token_type_embed=a__							,     token_type_vocab_size=predefined_args["""token_type_vocab_size"""]							,     use_classifier=a__							,     use_decoder=a__							,     )

       original_bort.load_parameters(a__							,     cast_dtype=a__							,     ignore_extra=a__    )
       lowerCamelCase__          =						original_bort._collect_params_with_prefix()

       # Build our config 🤗
       lowerCamelCase__          =						{
           'architectures': ['BertForMaskedLM'],
           'attention_probs_dropout_prob': predefined_args['dropout'],
           'hidden_act': 'gelu',
           'hidden_dropout_prob': predefined_args['dropout'],
           'hidden_size': predefined_args['embed_size'],
           'initializer_range': 0.02,
           'intermediate_size': predefined_args['hidden_size'],
           'layer_norm_eps': predefined_args['layer_norm_eps'],
           'max_position_embeddings': predefined_args['max_length'],
           'model_type': 'bort',
           'num_attention_heads': predefined_args['num_heads'],
           'num_hidden_layers': predefined_args['num_layers'],
           'pad_token_id': 1,  # 2 = BERT, 1 = RoBERTa
           'type_vocab_size': 1,  # 2 = BERT, 1 = RoBERTa
           'vocab_size': len(a__    ),
       }

       lowerCamelCase__          =						BertConfig.from_dict(a__    )
       lowerCamelCase__          =						BertForMaskedLM(a__    )
       hf_bort_model.eval()

       # Parameter mapping table (Gluonnlp to Transformers)
       # * denotes layer index
       #
       # | Gluon Parameter                                                | Transformers Parameter
       # | -------------------------------------------------------------- | ----------------------
       # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
       # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
       # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
       # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
       # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
       # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
       # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
       # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
       # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
       # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
       # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
       # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
       # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
       # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

       # Helper function to convert MXNET Arrays to PyTorch
       def to_torch(__lowercase    ) -> nn.Parameter:
              return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()    )    )

       # Check param shapes and map new HF param back
       def check_and_map_params(__lowercase							,     __lowercase    ):
              lowerCamelCase__          =						hf_param.shape

              lowerCamelCase__          =						to_torch(params[gluon_param]    )
              lowerCamelCase__          =						gluon_param.shape

              assert (
                  shape_hf == shape_gluon
              ), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

              return gluon_param

       lowerCamelCase__          =						check_and_map_params(
           hf_bort_model.bert.embeddings.word_embeddings.weight							,     """word_embed.0.weight"""    )
       lowerCamelCase__          =						check_and_map_params(
           hf_bort_model.bert.embeddings.position_embeddings.weight							,     """encoder.position_weight"""    )
       lowerCamelCase__          =						check_and_map_params(
           hf_bort_model.bert.embeddings.LayerNorm.bias							,     """encoder.layer_norm.beta"""    )
       lowerCamelCase__          =						check_and_map_params(
           hf_bort_model.bert.embeddings.LayerNorm.weight							,     """encoder.layer_norm.gamma"""    )

       # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
       lowerCamelCase__          =						torch.zeros_like(
           hf_bort_model.bert.embeddings.token_type_embeddings.weight.data    )

       for i in range(hf_bort_config.num_hidden_layers    ):
              lowerCamelCase__          =						hf_bort_model.bert.encoder.layer[i]

              # self attention
              lowerCamelCase__          =						layer.attention.self

              lowerCamelCase__          =						check_and_map_params(
                  self_attn.key.bias.data							,     f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""    )

              lowerCamelCase__          =						check_and_map_params(
                  self_attn.key.weight.data							,     f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""    )
              lowerCamelCase__          =						check_and_map_params(
                  self_attn.query.bias.data							,     f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""    )
              lowerCamelCase__          =						check_and_map_params(
                  self_attn.query.weight.data							,     f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""    )
              lowerCamelCase__          =						check_and_map_params(
                  self_attn.value.bias.data							,     f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""    )
              lowerCamelCase__          =						check_and_map_params(
                  self_attn.value.weight.data							,     f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""    )

              # self attention output
              lowerCamelCase__          =						layer.attention.output

              lowerCamelCase__          =						check_and_map_params(
                  self_output.dense.bias							,     f"""encoder.transformer_cells.{i}.proj.bias"""    )
              lowerCamelCase__          =						check_and_map_params(
                  self_output.dense.weight							,     f"""encoder.transformer_cells.{i}.proj.weight"""    )
              lowerCamelCase__          =						check_and_map_params(
                  self_output.LayerNorm.bias							,     f"""encoder.transformer_cells.{i}.layer_norm.beta"""    )
              lowerCamelCase__          =						check_and_map_params(
                  self_output.LayerNorm.weight							,     f"""encoder.transformer_cells.{i}.layer_norm.gamma"""    )

              # intermediate
              lowerCamelCase__          =						layer.intermediate

              lowerCamelCase__          =						check_and_map_params(
                  intermediate.dense.bias							,     f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""    )
              lowerCamelCase__          =						check_and_map_params(
                  intermediate.dense.weight							,     f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""    )

              # output
              lowerCamelCase__          =						layer.output

              lowerCamelCase__          =						check_and_map_params(
                  bert_output.dense.bias							,     f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""    )
              lowerCamelCase__          =						check_and_map_params(
                  bert_output.dense.weight							,     f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""    )
              lowerCamelCase__          =						check_and_map_params(
                  bert_output.LayerNorm.bias							,     f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""    )
              lowerCamelCase__          =						check_and_map_params(
                  bert_output.LayerNorm.weight							,     f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""    )

       # Save space and energy 🎄
       hf_bort_model.half()

       # Compare output of both models
       lowerCamelCase__          =						RobertaTokenizer.from_pretrained("""roberta-base"""    )

       lowerCamelCase__          =						tokenizer.encode_plus(a__    )['input_ids']

       # Get gluon output
       lowerCamelCase__          =						mx.nd.array([input_ids]    )
       lowerCamelCase__          =						original_bort(inputs=a__							,     token_types=[]    )

       # Get Transformer output (save and reload model again)
       hf_bort_model.save_pretrained(a__    )
       lowerCamelCase__          =						BertModel.from_pretrained(a__    )
       hf_bort_model.eval()

       lowerCamelCase__          =						tokenizer.encode_plus(a__							,     return_tensors="""pt"""    )
       lowerCamelCase__          =						hf_bort_model(**a__    )[0]

       lowerCamelCase__          =						output_gluon[0].asnumpy()
       lowerCamelCase__          =						output_hf[0].detach().numpy()

       lowerCamelCase__          =						np.max(np.abs(hf_layer - gluon_layer    )    ).item()
       lowerCamelCase__          =						np.allclose(a__							,     a__							,     atol=1e-3    )

       if success:
              print("""✔️ Both model do output the same tensors"""    )
       else:
              print("""❌ Both model do **NOT** output the same tensors"""    )
              print("""Absolute difference is:"""							,     a__    )


if __name__ == "__main__":
  __magic_name__				     =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __magic_name__				     =     parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)














"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"